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data <- read.csv("Live.csv")
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
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## filter, lag
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library(tidyr)
library(lattice)
library(MASS)
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## select
library(lubridate)
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library(mdsr)
## Loading required package: ggformula
## Loading required package: ggplot2
## Loading required package: ggstance
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## Attaching package: 'ggstance'
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## New to ggformula? Try the tutorials:
## learnr::run_tutorial("introduction", package = "ggformula")
## learnr::run_tutorial("refining", package = "ggformula")
## Loading required package: mosaicData
## Loading required package: Matrix
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## Note: If you use the Matrix package, be sure to load it BEFORE loading mosaic.
##
## Have you tried the ggformula package for your plots?
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## the 'tidyverse' package automatically.
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library(scales)
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library(psych)
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library(tidyverse)
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#library(GGally)
# View(data)
summary(data[,4:12])
## num_reactions num_comments num_shares num_likes
## Min. : 0.0 Min. : 0.0 Min. : 0.00 Min. : 0.0
## 1st Qu.: 17.0 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 17.0
## Median : 59.5 Median : 4.0 Median : 0.00 Median : 58.0
## Mean : 230.1 Mean : 224.4 Mean : 40.02 Mean : 215.0
## 3rd Qu.: 219.0 3rd Qu.: 23.0 3rd Qu.: 4.00 3rd Qu.: 184.8
## Max. :4710.0 Max. :20990.0 Max. :3424.00 Max. :4710.0
## num_loves num_wows num_hahas num_sads
## Min. : 0.00 Min. : 0.000 Min. : 0.0000 Min. : 0.0000
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median : 0.00 Median : 0.000 Median : 0.0000 Median : 0.0000
## Mean : 12.73 Mean : 1.289 Mean : 0.6965 Mean : 0.2437
## 3rd Qu.: 3.00 3rd Qu.: 0.000 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :657.00 Max. :278.000 Max. :157.0000 Max. :51.0000
## num_angrys
## Min. : 0.0000
## 1st Qu.: 0.0000
## Median : 0.0000
## Mean : 0.1132
## 3rd Qu.: 0.0000
## Max. :31.0000
new <- data
new = subset(new, select = -c(Column1, Column2, Column3, Column4) )
new <- separate(new, status_id, into = c("seller_id", "user_id") , sep = "_" )
new <- separate(new, status_published, into =c("date", "time"), sep = " ")
new
new %>% summarise_if(is.numeric, mean)
new %>% summarise_if(is.numeric, max)
pos_neg <- subset(new, select = c( status_type , num_wows, num_hahas, num_sads, num_angrys, num_loves ))
pos_neg$positive <- pos_neg$num_wows + pos_neg$num_hahas + pos_neg$num_loves
pos_neg$negative <- pos_neg$num_sads + pos_neg$num_angrys
out3 <- pos_neg %>%
group_by(status_type) %>%
summarise(positive = mean(positive), negative = mean(negative))
## `summarise()` ungrouping output (override with `.groups` argument)
plot_1 <- ggplot(data=out3, aes(x= status_type, y= positive)) +
geom_bar(stat="identity", fill="purple") +
geom_text(aes(label=positive), vjust=-0.3, size=3.5)+
theme_minimal()
pos_neg <- subset(new, select = c( status_type , num_likes , num_wows, num_hahas, num_sads, num_angrys, num_loves ))
pos_neg$positive <-pos_neg$num_likes + pos_neg$num_wows + pos_neg$num_hahas + pos_neg$num_loves
pos_neg$negative <- pos_neg$num_sads + pos_neg$num_angrys
pos_neg
out3 <- pos_neg %>%
group_by(status_type) %>%
summarise(positive = mean(positive), negative = mean(negative))
## `summarise()` ungrouping output (override with `.groups` argument)
plot_2 <- ggplot(data=out3, aes(x= status_type, y= positive)) +
geom_bar(stat="identity", fill="steelblue") +
geom_text(aes(label=positive), vjust=-0.3, size=3.5)+
theme_minimal()
plot_3 <- ggplot(data=out3, aes(x= status_type, y=negative)) +
geom_bar(stat="identity", fill="maroon") +
geom_text(aes(label=negative), vjust=-0.3, size=3.5)+
theme_minimal()
library(gridExtra)
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
grid.arrange(plot_1, plot_2, plot_3 ,ncol =3)
new$engagements <- new$num_likes + new$num_comments + new$num_shares
# aggregate(new$engagements, by=list(Status=new$status_type), FUN=sum)
out <- new %>%
group_by(status_type) %>%
summarise(engagements = mean(engagements))
## `summarise()` ungrouping output (override with `.groups` argument)
out<-arrange(out, engagements )
out
ggplot(data=out, aes(x= status_type, y=engagements)) +
geom_bar(stat="identity", fill="steelblue")+
geom_text(aes(label=engagements), vjust=-0.3, size=3.5)+
theme_minimal()
clean = subset(new, select = -c( num_wows, num_hahas, num_sads, num_angrys, num_loves ))
clean
status_count <- clean %>%
group_by(status_type) %>%
summarise_if(is.numeric, mean)
status_count
per_seller <- clean %>%
group_by(seller_id) %>%
summarise_if(is.numeric, sum)
per_seller <- arrange(per_seller, seller_id)
per_seller
by_time <- clean %>%
group_by(seller_id, date, time) %>%
summarise_if(is.numeric, sum)
by_time <- arrange(by_time, seller_id, date, time)
by_time
ggplot(data = by_time , aes(x = as.Date(date, "%m/%d/%Y") , y = num_reactions)) +
geom_point( size = 1) +
geom_line(color = "indianred3",
size=1 ) +
geom_smooth() +
scale_x_date(breaks = '1 year',
labels = date_format("%Y-%m-%d")) +
labs(title = "All seller reactions",
subtitle = "Yearly rate",
x = "Date",
y = "Num-reactions") +
#ylim(0, 8000) +
theme_minimal()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
ggplot(data = by_time , aes(x = as.Date(date, "%m/%d/%Y") , y = num_comments)) +
geom_point( size = 1) +
geom_line(color = "indianred3",
size=1 ) +
geom_smooth() +
scale_x_date(date_breaks = '1 year',
labels = date_format("%Y-%m-%d")) +
labs(title = "All seller comments",
subtitle = "Yearly rate",
x = "Date",
y = "Num-comments") +
# ylim(0, 10000) +
theme_minimal()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
ggplot(data = by_time , aes(x = as.Date(date, "%m/%d/%Y") , y = num_shares)) +
geom_point( size = 1) +
geom_line(color = "indianred3",
size=1 ) +
geom_smooth() +
scale_x_date(date_breaks = '1 year',
labels = date_format("%Y-%m-%d")) +
labs(title = "All seller shares received",
subtitle = "Yearly rate",
x = "Date",
y = "Num-shares") +
#ylim(0, 3000) +
theme_minimal()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
ggplot(data = by_time , aes(x = as.Date(date, "%m/%d/%Y") , y = num_comments)) +
geom_point(aes(shape = seller_id, color = seller_id), size = 1) +
scale_x_date(date_breaks = '1 year',
labels = date_format("%Y-%m-%d"))
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 9. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 749 rows containing missing values (geom_point).
new_copy <- clean
new_copy$date <- as.Date(new_copy$date, "%m/%d/%Y")
new_copy
before_live = filter(new_copy , date < "2016-06-04")
after_live = filter(new_copy , date >= "2016-06-04")
on_live_day = filter(new_copy , date == "2016-06-04")
before_live
by_time <- before_live %>%
group_by(seller_id, date, time) %>%
summarise_if(is.numeric, sum)
by_time <- arrange(by_time, seller_id, date, time)
by_time
after_live
pairs(~ num_likes + num_reactions + num_comments+ num_shares , data= clean,
lower.panel = panel.smooth)
pairs(~ num_reactions + num_comments+ num_shares , data= before_live,
lower.panel = panel.smooth)
pairs(~ num_reactions + num_comments+ num_shares , data= clean,
lower.panel = panel.smooth)
ggplot(data = clean, aes(x = num_reactions , y = num_likes )) +
geom_point() + geom_smooth(method = "lm", se = 0) +
xlab("Number of reactions") +
ylab("Number of likes")
## `geom_smooth()` using formula 'y ~ x'
ggplot(data = clean, aes(x = num_reactions , y = num_shares )) +
geom_point() + geom_smooth(method = "lm", se = 0) +
xlab("Number of reactions") +
ylab("Number of shares")
## `geom_smooth()` using formula 'y ~ x'
barplot(height=status_count$num_likes, names=status_count$status_type,
col="#69b3a2",
horiz=T, las=1
)
split_date <- clean
split_date$day <- factor(wday(as.Date(split_date$date, "%m/%d/%Y") -1))
split_date$day_date <- factor(day(as.Date(split_date$date, "%m/%d/%Y")))
split_date$month <- factor(format(as.Date(split_date$date, "%m/%d/%Y"),"%m"))
split_date$time_hour <- factor(format(strptime(split_date$time,"%H:%M"),"%H"))
glimpse(split_date)
## Rows: 7,050
## Columns: 14
## $ seller_id <chr> "246675545449582", "246675545449582", "246675545449582"…
## $ user_id <chr> "1649696485147474", "1649426988507757", "16487305885773…
## $ status_type <chr> "video", "photo", "video", "photo", "photo", "photo", "…
## $ date <chr> "4/22/2018", "4/21/2018", "4/21/2018", "4/21/2018", "4/…
## $ time <chr> "6:00", "22:45", "6:17", "2:29", "3:22", "2:14", "0:24"…
## $ num_reactions <int> 529, 150, 227, 111, 213, 217, 503, 295, 203, 170, 210, …
## $ num_comments <int> 512, 0, 236, 0, 0, 6, 614, 453, 1, 9, 2, 4, 4, 4, 11, 1…
## $ num_shares <int> 262, 0, 57, 0, 0, 0, 72, 53, 0, 1, 3, 0, 2, 0, 0, 30, 7…
## $ num_likes <int> 432, 150, 204, 111, 204, 211, 418, 260, 198, 167, 202, …
## $ engagements <int> 1206, 150, 497, 111, 204, 217, 1104, 766, 199, 177, 207…
## $ day <fct> 7, 6, 6, 6, 3, 3, 3, 2, 2, 3, 2, 1, 7, 7, 4, 7, 5, 1, 5…
## $ day_date <fct> 22, 21, 21, 21, 18, 18, 18, 17, 17, 11, 10, 9, 8, 8, 5,…
## $ month <fct> 04, 04, 04, 04, 04, 04, 04, 04, 04, 04, 04, 04, 04, 04,…
## $ time_hour <fct> 06, 22, 06, 02, 03, 02, 00, 07, 03, 04, 01, 02, 05, 02,…
pairs(~ engagements + time_hour + day_date + month + num_shares + num_comments , data= split_date,
lower.panel = panel.smooth)
out2 <- split_date %>%
group_by(time_hour) %>%
summarise(engagements = mean(engagements))
## `summarise()` ungrouping output (override with `.groups` argument)
out2<-arrange(out2, engagements )
out2
ggplot(data=out2, aes(x= time_hour, y=engagements)) +
geom_bar(stat="identity", fill="steelblue")+
geom_text(aes(label=engagements), vjust=-0.3, size=3.5)+
theme_minimal()
library(fastDummies)
split_date <- dummy_cols(split_date, select_columns = c('day', 'month', 'day_date', 'status_type', 'time_hour') )
split_date
set.seed(1234)
library(caTools)
trainIndex <- sample.split( split_date$engagements , SplitRatio = 0.7, group = NULL )
# Create separate training and test set records:
trainingSet <- split_date[trainIndex,]
testSet <- split_date[!trainIndex,]
# Assess the components (based on eigenvalues)
fa.parallel(trainingSet[,6:10], fa="pc", n.iter=100, show.legend=FALSE, main="Scree plot with parallel analysis")
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully
## In factor.scores, the correlation matrix is singular, an approximation is used
## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Parallel analysis suggests that the number of factors = NA and the number of components = 2
# Perform PCA, and derive the rotated components
pc <- principal(trainingSet[,6:10], nfactors=3, rotate="varimax", score=TRUE)
## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in principal(trainingSet[, 6:10], nfactors = 3, rotate = "varimax", :
## The matrix is not positive semi-definite, scores found from Structure loadings
pc
## Principal Components Analysis
## Call: principal(r = trainingSet[, 6:10], nfactors = 3, rotate = "varimax",
## scores = TRUE)
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC2 RC1 RC3 h2 u2 com
## num_reactions 0.98 0.13 0.12 1 1.1e-03 1.1
## num_comments -0.01 0.96 0.27 1 3.2e-05 1.2
## num_shares 0.10 0.42 0.90 1 4.5e-06 1.4
## num_likes 0.99 0.10 0.04 1 8.8e-04 1.0
## engagements 0.38 0.86 0.34 1 4.7e-05 1.7
##
## RC2 RC1 RC3
## SS loadings 2.11 1.87 1.02
## Proportion Var 0.42 0.37 0.20
## Cumulative Var 0.42 0.80 1.00
## Proportion Explained 0.42 0.38 0.20
## Cumulative Proportion 0.42 0.80 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0
## with the empirical chi square 0.01 with prob < NA
##
## Fit based upon off diagonal values = 1
# List the scoring formulas for the rotated components
round(pc$weights, 2)
##
## Loadings:
## RC2 RC1 RC3
## num_reactions 0.98 0.13 0.12
## num_comments 0.96 0.27
## num_shares 0.10 0.42 0.90
## num_likes 0.99 0.10
## engagements 0.38 0.86 0.34
##
## RC2 RC1 RC3
## SS loadings 2.095 1.864 1.015
## Proportion Var 0.419 0.373 0.203
## Cumulative Var 0.419 0.792 0.995
trainingSet <- cbind(trainingSet, pc$scores)
# Generate rotated component scores for testSet
testSet_RCscores <- predict(pc, testSet[,6:10], trainingSet[,6:10])
testSet_RCscores
## RC2 RC1 RC3
## 6 -0.249021916 -0.633535291 -0.461300444
## 8 0.203018949 0.340041159 0.147628545
## 9 -0.309314440 -0.657061527 -0.471827852
## 15 0.304746052 -0.479457736 -0.384348067
## 21 -0.187752216 -0.601912178 -0.442605491
## 25 -0.744221558 -0.773797115 -0.530868987
## 26 -1.022793323 -0.844327641 -0.562111253
## 30 -0.019821800 -0.023561915 -0.094118332
## 31 -0.823330010 -0.795414625 -0.541591195
## 38 -0.815547171 -0.728688275 -0.451907567
## 40 -0.376546560 -0.139944054 -0.164801671
## 46 0.518022025 0.376136874 0.091273294
## 48 -0.631125309 -0.618683714 -0.417956475
## 49 -0.314627411 -0.153618319 -0.071439417
## 50 0.197645112 0.264531887 0.035516988
## 55 -0.425475471 -0.328042014 -0.121753931
## 56 -0.241103524 -0.007191497 -0.042162382
## 59 -0.753454154 -0.602495227 -0.392921226
## 68 0.480060753 0.538039354 0.398308699
## 71 -0.938664248 -0.825388153 -0.557058300
## 75 -0.130701930 0.040860782 -0.026718671
## 79 0.042000601 0.158956403 0.066169087
## 80 -0.914130865 -0.817043209 -0.553237313
## 83 -0.553415703 -0.330837305 -0.165860460
## 87 0.055828430 -0.552137059 -0.414269039
## 89 0.131978905 -0.529679789 -0.408406939
## 93 1.698264607 -0.114347204 -0.197471675
## 95 1.278015820 -0.214033647 -0.250888973
## 102 -1.029729620 -0.832973704 -0.564027779
## 106 -0.549110983 -0.719964472 -0.504048631
## 107 -0.482602502 -0.665376660 -0.414544642
## 108 -0.369693788 -0.578093496 -0.286707734
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## 124 -0.762276450 -0.770642014 -0.530849558
## 125 0.058676222 -0.178276756 -0.170168401
## 126 0.202297423 -0.342577589 -0.235213219
## 130 0.579779848 0.957971699 0.325096019
## 134 -1.112163445 -0.852892773 -0.574601556
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## 141 -0.120183420 0.195415435 0.001854009
## 147 0.186546061 -0.521011331 -0.403280090
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## 188 0.236995550 0.484140945 0.137121809
## 189 1.517344793 -0.155730023 -0.214894665
## 199 1.404484952 1.485677987 0.856705381
## 209 2.065570740 1.713038108 0.784730513
## 211 -1.041379989 -0.834086544 -0.549237083
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## 215 0.939400366 0.697926931 0.273362696
## 216 -0.965043211 -0.813619736 -0.544208612
## 217 -0.762032457 -0.723158177 -0.484480554
## 218 0.123813833 -0.482803701 -0.378824880
## 224 1.475526391 -0.143644784 -0.218519628
## 234 0.886940483 -0.321950076 -0.304327101
## 236 0.454797128 -0.442139071 -0.359448871
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## 239 2.305927034 0.777548511 0.261118963
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## 1750 -0.610328884 -0.725236048 -0.508878618
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## 1779 -1.166158608 -0.879324466 -0.575179084
## 1780 -1.061977087 -0.851843899 -0.571716258
## 1781 -1.167127870 -0.882817233 -0.581572258
## 1782 -0.962529747 -0.831491491 -0.560248243
## 1786 -0.949121058 -0.826374019 -0.557955132
## 1787 -0.900147443 -0.808688903 -0.549896344
## 1789 -0.996657841 -0.835967048 -0.563394276
## 1797 -0.912406666 -0.807332703 -0.550093739
## 1802 -0.890825162 -0.803119365 -0.547669031
## 1803 -1.097829380 -0.866029962 -0.578005865
## 1805 -0.929219867 -0.810123759 -0.546583404
## 1811 -1.084420691 -0.860912491 -0.575712753
## 1812 -1.101341054 -0.862341061 -0.577023805
## 1818 -0.684395678 -0.743445602 -0.518380056
## 1824 -0.958155973 -0.830325140 -0.559658516
## 1835 -0.761336137 -0.777839333 -0.533120782
## 1842 -1.058752779 -0.857151219 -0.573222247
## 1844 -0.839839982 -0.774105324 -0.535942740
## 1845 -0.932731542 -0.806434857 -0.545601344
## 1864 -1.035914646 -0.847826695 -0.563880436
## 1868 -0.760474037 -0.772984080 -0.531548995
## 1869 -1.079472183 -0.856509304 -0.574075168
## 1872 -0.606242476 -0.725688115 -0.508812820
## 1873 -0.838627239 -0.790741567 -0.541116231
## 1877 -0.982567257 -0.828975227 -0.555231920
## 1881 -0.276485216 -0.628501264 -0.461439690
## 1882 -0.724909111 -0.760416434 -0.525783318
## 1885 -0.990272502 -0.823471773 -0.559137046
## 1889 -1.071874101 -0.860650273 -0.574991429
## 1892 -0.853248671 -0.779222796 -0.538235851
## 1893 -1.092773710 -0.862989262 -0.571546892
## 1896 -1.070149902 -0.850939767 -0.571847855
## 1900 -0.882077614 -0.800786662 -0.546489576
## 1902 -1.092593506 -0.860008358 -0.575844350
## 1914 -0.901296909 -0.815162573 -0.551992060
## 1917 -0.925527989 -0.810831757 -0.551862922
## 1924 -0.731070361 -0.748183378 -0.522247413
## 1925 -0.983823885 -0.834086412 -0.562149023
## 1926 -0.857909811 -0.782007565 -0.539349508
## 1927 -1.076247875 -0.861816624 -0.575581156
## 1933 -0.931625962 -0.821708614 -0.555596223
## 1935 -0.966041422 -0.827802589 -0.559266184
## 1937 -0.890250429 -0.799882529 -0.546621173
## 1942 -1.163723358 -0.885143649 -0.587375705
## 1945 -0.871893233 -0.790361871 -0.542690477
## 1946 -0.952632733 -0.822685118 -0.556973073
## 1948 -1.049430497 -0.851581681 -0.570994934
## 1949 -0.988772392 -0.838489598 -0.563786608
## 1951 -1.169957649 -0.877253982 -0.574720954
## 1959 -0.953782199 -0.829158788 -0.559068789
## 1961 -0.816471002 -0.783291393 -0.537643665
## 1962 -0.801912847 -0.771700251 -0.533254838
## 1963 -1.071874101 -0.860650273 -0.574991429
## 1966 -0.884040327 -0.778669994 -0.502916812
## 1968 -1.124359391 -0.874646488 -0.582068158
## 1969 -1.106864294 -0.869981083 -0.579709248
## 1972 -1.051217973 -0.838182273 -0.566869301
## 1974 -1.070617472 -0.855539088 -0.568074326
## 1978 -1.040395583 -0.847630561 -0.569291550
## 1979 -0.990272502 -0.823471773 -0.559137046
## 1981 -1.053516905 -0.851129614 -0.571060733
## 1982 -1.036883908 -0.851319462 -0.570273610
## 1987 -1.106002195 -0.865125830 -0.578137462
## 1988 -0.940724153 -0.802549821 -0.550030400
## 1989 -0.948546325 -0.823137184 -0.556907274
## 1990 -0.889388330 -0.795027276 -0.545049386
## 1995 -0.790228357 -0.776293286 -0.534105300
## 1999 -0.979450111 -0.832920061 -0.561559295
## 2004 -1.161210101 -0.874921279 -0.573541499
## 2005 -1.137480713 -0.878145541 -0.583837340
## 2017 -1.181218455 -0.889809054 -0.589734615
## 2020 -0.983823885 -0.834086412 -0.562149023
## 2021 -0.852898027 -0.800714292 -0.544981129
## 2025 -0.787310806 -0.738361681 -0.516775417
## 2026 -1.054379005 -0.855984867 -0.572632519
## 2027 -1.133106939 -0.876979190 -0.583247613
## 2032 -1.053804272 -0.852748032 -0.571584661
## 2033 -0.930476496 -0.815234944 -0.553500507
## 2034 -1.039635680 -0.829714658 -0.537391478
## 2035 -0.974214237 -0.826898456 -0.559397781
## 2039 -0.999020050 -0.825804476 -0.560316501
## 2042 -0.980024844 -0.836156896 -0.562607153
## 2053 -1.016515146 -0.830469881 -0.562675411
## 2056 -1.119698250 -0.871861719 -0.580954502
## 2057 -1.092593506 -0.860008358 -0.575844350
## 2058 -1.001318982 -0.838751817 -0.564507932
## 2059 -1.133106939 -0.876979190 -0.583247613
## 2062 -1.045344090 -0.852033747 -0.570929136
## 2063 -1.117686685 -0.860532795 -0.577286999
## 2068 -1.004255923 -0.831826080 -0.562478015
## 2070 -0.973639504 -0.823661621 -0.558349923
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## 2076 -1.083845958 -0.857675656 -0.574664896
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## 2083 -1.058465412 -0.855532801 -0.572698318
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## 2095 -1.092306139 -0.858389941 -0.575320421
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## 2260 -1.136905980 -0.874908706 -0.582789482
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## 2275 -1.133106939 -0.876979190 -0.583247613
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## 2409 -1.110375969 -0.866292181 -0.578727189
## 2411 -1.110375969 -0.866292181 -0.578727189
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## 2435 -1.061689720 -0.850225482 -0.571192329
## 2436 -1.123390129 -0.871153720 -0.575674984
## 2439 -1.119410884 -0.870243301 -0.580430573
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## 2456 -1.120623627 -0.853607058 -0.575257082
## 2457 -1.106864294 -0.869981083 -0.579709248
## 2464 -1.116537219 -0.854059125 -0.575191283
## 2467 -1.048281031 -0.845108010 -0.568899218
## 2476 -1.122347825 -0.863317564 -0.578400656
## 2477 -1.071362645 -0.834303524 -0.566674365
## 2481 -1.077460618 -0.845180381 -0.570407666
## 2482 -1.115037110 -0.869076950 -0.579840845
## 2483 -1.075098409 -0.855342953 -0.573485441
## 2484 -1.106576928 -0.868362665 -0.579185319
## 2487 -1.128158432 -0.872576004 -0.581610028
## 2489 -1.091049511 -0.853278756 -0.568403318
## 2492 -1.114749743 -0.867458532 -0.579316916
## 2498 -1.099904222 -0.854248972 -0.574404161
## 2502 -1.054408160 -0.838580829 -0.551423080
## 2504 -1.092944149 -0.838516862 -0.569099073
## 2512 -1.081834392 -0.846346732 -0.570997393
## 2514 -1.087250470 -0.855349240 -0.568861449
## 2516 -1.011566639 -0.826066694 -0.561037825
## 2517 -1.070787912 -0.831066688 -0.565626507
## 2520 -1.080046916 -0.859746140 -0.575123026
## 2524 -1.104852729 -0.858652159 -0.576041746
## 2536 -1.127476536 -0.870701654 -0.575740782
## 2544 -1.042650629 -0.838830474 -0.561392388
## 2545 -1.124072024 -0.873028070 -0.581544229
## 2547 -1.131957473 -0.870505520 -0.581151897
## 2548 -1.115675119 -0.849203872 -0.573619497
## 2549 -1.124359391 -0.874646488 -0.582068158
## 2551 -1.106864294 -0.869981083 -0.579709248
## 2556 -1.106002195 -0.865125830 -0.578137462
## 2560 -1.102203154 -0.867196314 -0.578595592
## 2564 -1.132137677 -0.873486423 -0.576854439
## 2571 -1.086963103 -0.853730822 -0.568337520
## 2572 -1.062551820 -0.855080735 -0.572764116
## 2573 -1.093869526 -0.820262202 -0.563401653
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## 6128 -1.106002195 -0.865125830 -0.578137462
## 6131 -1.132819573 -0.875360773 -0.582723684
## 6134 -1.084995424 -0.864149326 -0.576760611
## 6138 0.369139911 2.146468040 2.136453958
## 6140 -1.146228262 -0.880478244 -0.585016795
## 6141 -1.159349584 -0.883977298 -0.586785977
## 6147 -0.256182885 0.337678157 0.433862610
## 6148 -1.128733165 -0.875812839 -0.582657885
## 6149 -1.087372570 -0.865044096 -0.577109032
## 6155 0.320105820 1.816007574 1.844786760
## 6157 -0.104853356 0.511237069 1.158911951
## 6158 -1.091746344 -0.866210447 -0.577698760
## 6160 -1.061689720 -0.850225482 -0.571192329
## 6161 -1.124359391 -0.874646488 -0.582068158
## 6169 -0.462595894 0.061798443 0.274901059
## 6171 0.415304756 -0.429265599 -0.346593994
## 6177 0.377855368 -0.418495427 -0.314564470
## 6178 -0.696856141 -0.751156284 -0.521170019
## 6185 4.937960236 0.814637126 0.285174162
## 6188 7.302729693 1.583341788 0.675598519
## 6193 10.367159036 2.692410179 1.184849808
## 6195 4.356913312 0.755338294 0.258341834
## 6196 2.535476622 0.286640649 0.028328344
## 6198 17.521139003 4.124937701 1.960320140
## 6199 16.698279762 4.032501280 1.888577138
## 6206 4.283963438 0.928109677 0.308780066
## 6209 -0.820715689 -0.781583377 -0.526399432
## 6210 1.501201427 -0.009629181 -0.096693952
## 6212 -0.819365909 -0.777467422 -0.519899143
## 6220 7.470859751 1.539223795 0.726612369
## 6221 3.129012981 0.498904777 0.288255751
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## 6224 6.231095630 1.123951607 0.438133214
## 6226 0.176769357 -0.469755538 -0.375154039
## 6227 -0.700936449 -0.755675582 -0.517771987
## 6232 -0.204371202 -0.602089586 -0.438154412
## 6234 5.894524173 1.047813549 0.397091142
## 6238 0.093203720 -0.524033649 -0.409362979
## 6240 6.383637182 1.383453210 0.532860930
## 6241 6.015712903 1.108792944 0.427858212
## 6248 7.483605974 1.473572424 0.601653613
## 6253 2.333863031 0.295834986 0.009816985
## 6254 1.021246909 -0.276622573 -0.268411932
## 6262 9.370411067 2.087299484 0.975038858
## 6263 -0.819365909 -0.777467422 -0.519899143
## 6268 2.695291685 0.453418850 0.125390417
## 6278 -0.632994320 -0.553986579 -0.418887678
## 6282 -0.079924467 2.556126004 1.581131871
## 6284 -1.040488734 -0.846635330 -0.568874736
## 6291 -1.149632774 -0.878151828 -0.579213348
## 6294 -1.004744530 -0.842511379 -0.565594512
## 6299 -1.157411060 -0.876991763 -0.573999629
## 6302 -1.031453819 -0.842684210 -0.567171352
## 6304 0.242312772 2.954932290 2.737485082
## 6311 -0.867101154 -0.761853541 -0.533366871
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## 6320 -1.026520249 -0.849338365 -0.568959963
## 6339 -1.036962122 -0.839266890 -0.566430599
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## 6356 -0.938470033 -0.822774505 -0.556117557
## 6357 -0.977804126 -0.811156983 -0.554572711
## 6370 1.014104744 4.810622990 4.367617693
## 6373 -0.701482509 -0.592270133 -0.434747251
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## 6386 0.534046133 3.265747638 2.669120010
## 6397 -1.132912724 -0.874365542 -0.582306870
## 6404 -0.802677410 -0.705607345 -0.512495076
## 6406 -0.861290547 -0.752595101 -0.530157499
## 6408 -0.892355146 -0.810216223 -0.549871862
## 6409 -0.980511844 -0.771861725 -0.437451001
## 6416 -0.926259150 -0.789963449 -0.545224759
## 6422 -0.995903830 -0.841173906 -0.564831871
## 6425 -1.049143131 -0.849963263 -0.570471005
## 6428 -0.827753050 -0.773838318 -0.529673515
## 6437 -0.884215733 -0.765895758 -0.535618666
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## 6440 -0.840522803 -0.787404271 -0.539999980
## 6443 -1.004997981 -0.821741546 -0.522304370
## 6451 -1.154975810 -0.882810947 -0.586196250
## 6455 -1.115611843 -0.872313785 -0.580888703
## 6456 -0.760047101 -0.701519270 -0.492978913
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## 6462 -0.756674108 -0.776903688 -0.531884578
## 6463 -0.861910092 -0.789003961 -0.541483945
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## 6476 -0.676402178 -0.749179763 -0.513828453
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## 6507 -0.954601545 -0.795597085 -0.516864139
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## 6521 -1.080621649 -0.862982975 -0.576170884
## 6525 -1.063126553 -0.858317570 -0.573811974
## 6527 -1.013779445 -0.846462499 -0.567297895
## 6532 -1.095594650 -0.841397305 -0.564733221
## 6533 -1.003408761 -0.838028169 -0.564332425
## 6537 -1.101521258 -0.865321964 -0.572726347
## 6541 -1.123390129 -0.871153720 -0.575674984
## 6543 -1.138313864 -0.872310708 -0.576744729
## 6544 -1.041638200 -0.853109001 -0.570970452
## 6546 -1.028136360 -0.848986760 -0.569094155
## 6548 -1.087932365 -0.857223589 -0.574730694
## 6554 -0.415478716 -0.429464787 -0.379225063
## 6557 -1.157907785 -0.864187045 -0.549016660
## 6562 1.569675067 8.242868310 5.426569429
## 6575 -1.122362763 -0.874374906 -0.581826852
## 6579 -0.669439022 -0.603466889 -0.426149875
## 6581 -0.614754037 0.625286037 0.482587534
## 6588 -0.735330171 0.546187967 0.257449442
## 6590 -1.059191400 -0.851060453 -0.560784320
## 6592 -1.084816628 -0.688546697 -0.473964030
## 6595 -0.468350640 0.860550871 0.454155065
## 6596 -0.489380230 1.058261215 0.510707081
## 6597 -1.080621649 -0.862982975 -0.576170884
## 6602 -0.743142578 0.122952071 -0.058182851
## 6610 -0.567324155 -0.715553421 -0.492719170
## 6613 -0.954147780 -0.818724634 -0.555749707
## 6621 -0.669766333 -0.750360265 -0.519486063
## 6627 -0.701574666 -0.707708378 -0.481676801
## 6629 -0.789832014 -0.766461881 -0.530449442
## 6632 -1.084995424 -0.864149326 -0.576760611
## 6634 -0.989596705 -0.816626060 -0.556731631
## 6643 -1.070904839 -0.857157505 -0.568598255
## 6645 -0.828913068 -0.719425941 -0.512447064
## 6648 -0.979450111 -0.832920061 -0.561559295
## 6649 0.247676392 4.003747410 1.832326115
## 6650 -0.941952759 -0.808395517 -0.546471100
## 6652 -0.978480849 -0.829427293 -0.555166121
## 6656 -0.070671955 -0.289001426 -0.327848073
## 6657 -0.977739962 -0.836116021 -0.561719371
## 6658 -0.815911206 -0.791111900 -0.540022003
## 6661 -1.084708057 -0.862530909 -0.576236682
## 6671 -0.345988543 -0.459158063 -0.401090300
## 6673 -0.246849951 -0.518179872 -0.382822925
## 6675 -0.776353023 -0.778001090 -0.533773713
## 6677 0.467187564 0.174547149 -0.112902971
## 6678 0.076192362 -0.050430731 -0.219342480
## 6683 0.027617317 -0.067586349 -0.185094274
## 6685 -0.980965158 -0.828959577 -0.560335930
## 6691 -1.067500327 -0.859483921 -0.574401702
## 6692 -1.119016355 -0.869987369 -0.575085257
## 6700 -0.839776705 -0.797215238 -0.543211947
## 6703 -0.874852137 -0.800946709 -0.545738580
## 6704 -0.870486275 -0.804384466 -0.546923224
## 6708 -0.261165138 -0.617512872 -0.456714086
## 6710 -0.638500947 -0.603446861 -0.335408636
## 6714 -0.546121317 -0.689114076 -0.494025588
## 6717 -0.920687570 -0.816490682 -0.553234718
## 6722 0.314537717 -0.066922441 -0.180766232
## 6725 -0.971277296 -0.833824193 -0.561427698
## 6726 -0.655977127 -0.744619606 -0.517085838
## 6729 -0.904565310 -0.803508557 -0.532702828
## 6739 0.013596314 -0.130753573 -0.226935845
## 6740 -0.035614945 2.210332458 0.875971507
## 6749 -0.599576795 -0.718027655 -0.505683485
## 6759 0.940975560 0.346368460 -0.060608266
## 6760 0.484259282 0.647547724 0.099858809
## 6766 -0.086466491 -0.211278425 -0.314791450
## 6769 0.211577539 0.036904502 -0.216054807
## 6781 -0.565509492 0.724825756 0.218555197
## 6783 -0.658914068 -0.737693869 -0.515055920
## 6793 -0.401748095 -0.625754816 -0.465815271
## 6802 0.279977889 -0.195928819 -0.285571304
## 6807 0.264639138 0.232938944 -0.136146035
## 6816 -0.655092177 -0.724102903 -0.510313483
## 6817 -0.811343217 -0.787331900 -0.538491533
## 6818 0.225650271 0.291592207 -0.014725643
## 6819 -0.421521976 -0.279952370 -0.358868893
## 6821 -1.002546662 -0.833172916 -0.562760638
## 6830 -0.577176633 -0.535235572 -0.410307556
## 6841 -0.113106818 -0.183344284 -0.260425992
## 6843 -0.931037218 -0.818839034 -0.549310163
## 6847 -0.888419993 -0.802959106 -0.536844207
## 6858 -1.137480713 -0.878145541 -0.583837340
## 6859 -1.141854488 -0.879311893 -0.584427068
## 6861 -0.917793795 -0.805645125 -0.539293397
## 6864 -1.097542013 -0.864411545 -0.577481936
## 6865 -0.810862324 -0.215479296 -0.295141748
## 6870 0.291660592 2.817926662 1.529582163
## 6875 -0.961760799 -0.825641008 -0.558259642
## 6876 -1.084420691 -0.860912491 -0.575712753
## 6877 -1.054817626 -0.849894102 -0.560194592
## 6880 -0.973712546 -0.828005010 -0.549231077
## 6884 -0.803804410 -0.033092054 -0.179695605
## 6888 -0.837527728 -0.193904699 -0.284627995
## 6889 -0.247701003 1.812484773 0.718346046
## 6890 -1.083379313 -0.864500932 -0.576626420
## 6896 -1.003501913 -0.837032938 -0.563915610
## 6898 -1.054550370 -0.842937065 -0.568372695
## 6899 -1.061495505 -0.847611834 -0.570251586
## 6901 -0.821670014 -0.774018802 -0.529366410
## 6912 -1.104867666 -0.869709501 -0.579467942
## 6921 -0.757952867 -0.758157601 -0.515940417
## 6924 -0.268290625 2.039124469 0.542389171
## 6926 -1.028229511 -0.847991530 -0.568677341
## 6927 -1.133106939 -0.876979190 -0.583247613
## 6930 -0.361341411 -0.626298877 -0.422892802
## 6934 -0.901355220 -0.780354495 -0.509573180
## 6935 -0.504036702 0.788364943 0.236218708
## 6943 -1.043619891 -0.842323241 -0.567785562
## 6952 -1.154975810 -0.882810947 -0.586196250
## 6953 -1.106576928 -0.868362665 -0.579185319
## 6957 -1.044633245 -0.839469311 -0.556395493
## 6958 -1.079652387 -0.859490208 -0.569777710
## 6962 -1.020888921 -0.831636232 -0.563265138
## 6967 -1.091229715 -0.856259659 -0.564105860
## 6968 -1.112645952 -0.868549436 -0.574254223
## 6970 -1.128733165 -0.875812839 -0.582657885
## 6973 -1.154006548 -0.879318179 -0.579803076
## 6977 -1.006453792 -0.841164543 -0.565311889
## 6978 1.251853263 7.120860822 2.933259274
## 6983 -1.185592229 -0.890975405 -0.590324342
## 6989 -1.163723358 -0.885143649 -0.587375705
## 6994 -1.151421175 -0.876177017 -0.573275710
## 6996 1.800875243 -0.072220385 -0.143523858
## 7000 -0.300970466 -0.642919337 -0.446082242
## 7004 -0.384228396 -0.669068993 -0.479474880
## 7005 0.039530970 -0.543128202 -0.397288351
## 7007 -0.702538548 -0.755691232 -0.512667977
## 7011 -1.145078796 -0.874004573 -0.582921079
## 7019 -1.084995424 -0.864149326 -0.576760611
## 7022 -0.880606660 -0.798400449 -0.529929699
## 7025 -1.133106939 -0.876979190 -0.583247613
## 7027 -1.146228262 -0.880478244 -0.585016795
## 7032 -0.966903522 -0.832657842 -0.560837971
## 7035 -1.159349584 -0.883977298 -0.586785977
## 7039 -1.181218455 -0.889809054 -0.589734615
## 7042 -1.150982554 -0.882267783 -0.585713637
## 7043 -1.168097132 -0.886310000 -0.587965432
## 7047 -1.120366135 -0.874103324 -0.581585545
## 7048 -1.179221827 -0.889537472 -0.589493308
## 7049 0.369620370 -0.387113758 -0.237089881
## 7050 -1.111238068 -0.871147434 -0.580298976
testSet <- cbind(testSet,testSet_RCscores)
testSet
#fitted <- lm( num_comments ~ status_type + num_shares + num_reactions ,
# data= trainingSet, na.action = na.exclude)
fitted <- lm( engagements ~ num_reactions ,
data= trainingSet, na.action = na.exclude)
summary(fitted)
##
## Call:
## lm(formula = engagements ~ num_reactions, data = trainingSet,
## na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1852.3 -245.9 -221.9 -216.2 20634.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 216.17353 17.02574 12.70 <2e-16 ***
## num_reactions 1.35819 0.03078 44.12 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1082 on 5097 degrees of freedom
## Multiple R-squared: 0.2764, Adjusted R-squared: 0.2762
## F-statistic: 1947 on 1 and 5097 DF, p-value: < 2.2e-16
par(mfrow=c(2,2))
plot(fitted)
model.regression <- stepAIC(fitted, direction="forward")
## Start: AIC=71255.07
## engagements ~ num_reactions
model.regression
##
## Call:
## lm(formula = engagements ~ num_reactions, data = trainingSet,
## na.action = na.exclude)
##
## Coefficients:
## (Intercept) num_reactions
## 216.174 1.358
#drawback on such models on COUNTS data, results are not integers
testSet.predictRegression <- predict(model.regression, testSet)
summary(testSet.predictRegression)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 216.2 237.9 281.4 451.6 458.6 6076.8
Dataset.Predict <- cbind(testSet.predictRegression,
testSet$num_comments)
#glimpse(Dataset.Predict)
Dataset.Predict <- as.data.frame(Dataset.Predict)
#glimpse(Dataset.Predict)
names(Dataset.Predict) <- c("MLR","Target")
ggplot (data = Dataset.Predict) +
geom_point (mapping = aes (x = Target,
y = MLR-Target ), shape = 21, fill = "red")
library(MLmetrics)
##
## Attaching package: 'MLmetrics'
## The following object is masked from 'package:psych':
##
## AUC
## The following object is masked from 'package:base':
##
## Recall
R2_Score(testSet.predictRegression, testSet$engagements)
## [1] 0.4638593
RMSE(testSet.predictRegression, testSet$engagements)
## [1] 382.4785
MAE(testSet.predictRegression, testSet$engagements)
## [1] 290.5487
#R2_Score(testSet.predictRegression, testSet$num_comments)
#RMSE(testSet.predictRegression, testSet$num_comments)
#MAE(testSet.predictRegression, testSet$num_comments)
library(randomForest)
## randomForest 4.6-14
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
## The following object is masked from 'package:gridExtra':
##
## combine
## The following object is masked from 'package:psych':
##
## outlier
## The following object is masked from 'package:ggplot2':
##
## margin
## The following object is masked from 'package:dplyr':
##
## combine
trainingSet_1 = subset(trainingSet, select = -c( engagements, seller_id, user_id, status_type, date, time, day_date, month, time_hour, day , num_comments, num_shares, num_likes ))
testSet_1 = subset(testSet, select = -c( engagements, seller_id, user_id, status_type, date, time, day_date, month, time_hour, day, num_comments, num_shares, num_likes))
model_RF <- randomForest(trainingSet_1, trainingSet$engagements , ntree= 300 , mtry= 15 , importance=TRUE)
# Importance of features
round(importance(model_RF), 2)
## %IncMSE IncNodePurity
## num_reactions 6.90 8.700846e+08
## day_1 -0.80 1.167405e+07
## day_2 1.57 5.501528e+06
## day_3 -0.59 1.248511e+07
## day_4 -2.64 4.492804e+06
## day_5 0.51 8.985483e+06
## day_6 -0.28 6.057806e+06
## day_7 3.08 7.981946e+06
## month_01 1.22 7.221657e+06
## month_02 3.55 6.420981e+06
## month_03 3.82 5.390216e+06
## month_04 2.94 6.417051e+06
## month_05 3.93 1.969583e+07
## month_06 0.48 5.810320e+06
## month_07 4.19 4.429266e+06
## month_08 5.99 1.217708e+07
## month_09 5.35 6.074685e+07
## month_10 3.70 3.282845e+06
## month_11 3.73 4.348332e+07
## month_12 1.63 1.735612e+07
## day_date_1 0.96 1.122632e+06
## day_date_2 1.52 3.449603e+06
## day_date_3 0.87 2.345366e+06
## day_date_4 0.66 2.637577e+06
## day_date_5 -0.05 2.619702e+06
## day_date_6 -0.90 1.526983e+06
## day_date_7 -1.40 4.063760e+06
## day_date_8 0.23 5.222812e+06
## day_date_9 -1.29 2.228991e+07
## day_date_10 -1.47 2.008091e+06
## day_date_11 -0.31 1.855391e+06
## day_date_12 -0.40 1.795706e+06
## day_date_13 -0.70 2.961523e+06
## day_date_14 0.54 1.744096e+06
## day_date_15 0.56 1.643956e+06
## day_date_16 -1.35 2.105987e+06
## day_date_17 0.57 1.014645e+06
## day_date_18 -1.17 1.973685e+06
## day_date_19 -1.99 2.414417e+06
## day_date_20 0.16 2.153826e+06
## day_date_21 -1.29 5.186580e+06
## day_date_22 -0.62 2.080798e+06
## day_date_23 1.15 2.076775e+06
## day_date_24 0.05 5.094217e+06
## day_date_25 -1.99 2.651283e+07
## day_date_26 -1.44 1.694533e+06
## day_date_27 -0.87 2.986329e+06
## day_date_28 0.29 2.119161e+06
## day_date_29 0.09 1.445584e+06
## day_date_30 0.57 3.871198e+07
## day_date_31 -0.87 1.369833e+06
## status_type_link -0.16 2.967544e+05
## status_type_photo 5.21 1.393236e+08
## status_type_status 2.34 4.987928e+06
## status_type_video 6.88 1.912205e+08
## time_hour_00 1.46 1.680451e+06
## time_hour_01 2.80 1.138232e+07
## time_hour_02 2.78 4.138548e+06
## time_hour_03 1.86 2.203172e+06
## time_hour_04 1.66 1.406328e+06
## time_hour_05 2.52 4.364614e+06
## time_hour_06 1.32 5.089649e+06
## time_hour_07 2.80 8.494582e+06
## time_hour_08 1.03 4.144169e+07
## time_hour_09 0.38 5.815810e+06
## time_hour_10 2.32 4.189220e+07
## time_hour_11 0.75 2.557686e+06
## time_hour_12 1.05 8.108224e+05
## time_hour_13 -1.62 1.368849e+05
## time_hour_14 0.89 2.546498e+04
## time_hour_15 1.28 6.003099e+04
## time_hour_16 -1.00 2.999200e+03
## time_hour_17 0.31 3.268000e+01
## time_hour_18 1.00 3.721000e+01
## time_hour_19 1.61 9.568690e+04
## time_hour_20 -1.08 1.625086e+05
## time_hour_21 2.61 3.805771e+07
## time_hour_22 1.96 1.980796e+06
## time_hour_23 -0.51 2.809815e+06
## RC2 7.08 1.172623e+09
## RC1 15.62 3.021154e+09
## RC3 14.08 2.135301e+09
varImpPlot(model_RF, n.var=10)
testSet.predictRF <- predict(model_RF, testSet_1)
R2_Score(testSet.predictRF, testSet$engagements)
## [1] 0.9869683
RMSE(testSet.predictRF, testSet$engagements)
## [1] 59.63057
MAE(testSet.predictRF, testSet$engagements)
## [1] 16.54485
library(e1071)
model_svm <- svm(trainingSet_1, trainingSet$engagements, kernel="linear")
model_svm <- svm(trainingSet_1, trainingSet$engagements, kernel="linear")
# Make prediction
testSet.predictSVM <- predict(model_svm, testSet_1)
R2_Score(testSet.predictSVM, testSet$engagements)
## [1] 0.9913074
RMSE(testSet.predictSVM, testSet$engagements)
## [1] 48.7014
MAE(testSet.predictSVM, testSet$engagements)
## [1] 38.0316
# cbind predicted value vectors into a dataset
Dataset.Predict <- cbind(testSet.predictRegression,
# testSet.predictMLP,
testSet.predictSVM,
testSet.predictRF,
testSet$engagements)
# Convert matrix to data frame
Dataset.Predict <- as.data.frame(Dataset.Predict)
names(Dataset.Predict) <- c("REG", "SVM", "RF","Target") # add header names
# Plot residual (i.e. prediction errors) vs. target values
ggplot (data = Dataset.Predict) +
geom_point (mapping = aes (x = Target,
y = REG-Target), shape = 21, fill = "red") +
#geom_point (mapping = aes (x = Target,
# y = MLP-Target), shape = 21, fill = "yellow") +
geom_point (mapping = aes (x = Target,
y = SVM-Target), shape = 21, fill = "green") +
geom_point (mapping = aes (x = Target,
y = RF-Target), shape = 21, fill = "blue")